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End-to-end QP-based policies: A unified perspective on robust control and robot learning — arXiv:2609.31905

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31905
License (as archived): CC0-1.0
Archived: 2026-10-08T00:16:50.868Z
SHA-256 of the archived text: 39dbadaf2400d70d3fc8ee728ea5bf39efa130cc5d54b299ccf62809b2a2c759
Stable URL of this page: https://metraipolis.com/documento/dc_d0da66024247

How to cite

End-to-end QP-based policies: A unified perspective on robust control and robot learning — arXiv:2609.31905 — arXiv (en), https://arxiv.org/abs/2609.31905 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d0da66024247 (Metraipolis archive dc_d0da66024247, sha256 39dbadaf2400d70d3fc8ee728ea5bf39efa130cc5d54b299ccf62809b2a2c759)

BibTeX

@misc{dcd0da66024247,
  title = {End-to-end QP-based policies: A unified perspective on robust control and robot learning — arXiv:2609.31905},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31905},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d0da66024247; sha256 39dbadaf2400d70d3fc8ee728ea5bf39efa130cc5d54b299ccf62809b2a2c759},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31905. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We present an end-to-end QP-based policy framework that enables systematic policy construction with minimal domain-specific design, while preserving the transparency and interpretability of model-based control. The proposed policy representation supports both domain-randomized model-based auto-tuning, where policy parameters are optimized over distributions of disturbances and model variations, and black-box policy construction, where the problem is formulated in terms of a (possibly) unknown model without requiring explicit notions of states, inputs, or the underlying system dynamics. We establish connections to existing policy representations and control paradigms, including robust control, multilayer perceptrons, and robot learning, and interpret our end-to-end QP policies as a common abstraction of these approaches. We validate the resulting framework in both simulation and hardware, demonstrating a broad range of applications spanning robustness, automatic policy tuning, and control under unknown system dynamics.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.31905 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T23:52:16.387Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).